Abstract: Abstract： An improved bald eagle search algorithm is proposed to address the problems that the bald eagle search （BES） algorithm is easy to slip into local optimum and low solution accuracy. Firstly， a Circle chaotic map is used in place of the original algorithm’s randomly generated initial population to increase the initial population’s diversity. Secondly， in the search selection space phase， adaptive weight is combined to update the bald eagle individual position and balance the search and development ability of the algorithm. Finally， the elite differential variation is fused with a somersault foraging strategy and is used to update the positions generated by bald eagle leader individuals in the subsequent stages. The ability of the algorithm to jump out of local optimum is improved. The method underwent comparative simulation tests in some standard test functions， and the Random Forest classification parameters were optimized using the suggested strategy in this research. The experimental results demonstrate that the improved algorithm outperforms the conventional algorithm in terms of solution efficiency， solution accuracy， and classification accuracy.

Key words: Key words： bald eagle search algorithm, Circle chaotic map, adaptive weight, somersault foraging strategy, elite differential mutation